Determining Insulin Resistance Cutoffs in Mexican Adults: Percentile Distribution vs. Receiver Operating Characteristic Curve Analysis

Introduction Insulin resistance (IR) plays a key role in the development of metabolic syndrome (MetS), type 2 diabetes, and cardiovascular disease. The Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) is widely used to estimate IR, but there is no consensus on the optimal cutoff values for identifying individuals at risk. This study aims to compare two methodologies, percentile distributions and receiver operating characteristic (ROC) curve analysis, for determining optimal HOMA-IR cutoff values in a population from Mexico City. Methods This cross-sectional study included 765 adults recruited from a hospital outpatient clinic in Mexico City. Participants were divided into two groups: a reference group of individuals with healthy weight and fasting plasma glucose and a MetS group of overweight or obese individuals classified based on the presence or absence of MetS. HOMA-IR values were analyzed using the 75th percentile in the reference group and ROC curve analysis in the MetS group. Optimal cutoffs were determined using the Youden index. Results We include a total of 765 patients, 218 subjects in the reference group and 547 for the ROC curve analysis. HOMA-IR percentiles 75th and 90th were 2.72 and 3.71, respectively. ROC curve analysis yielded higher cutoff values for MetS diagnosis than the percentile-based method. The percentile-based approach allowed for earlier identification of individuals at risk, including those without clinical manifestations of MetS. Conclusions This study highlights the variability in HOMA-IR cutoff values across methodologies and emphasizes the importance of population-specific reference values. A percentile-based approach proves effective for early detection of IR, facilitating preventive interventions during the preclinical stage. These findings support using percentile-based cutoffs as a practical tool for improving risk assessment and guiding clinical decision-making.